Overview

Dataset statistics

Number of variables24
Number of observations98913
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.5 MiB
Average record size in memory164.0 B

Variable types

Numeric13
Categorical7
Boolean4

Alerts

type has constant value "user"Constant
country has a high cardinality: 200 distinct valuesHigh cardinality
countryCode has a high cardinality: 199 distinct valuesHigh cardinality
productsListed is highly overall correlated with productsSold and 1 other fieldsHigh correlation
productsSold is highly overall correlated with productsListed and 1 other fieldsHigh correlation
productsPassRate is highly overall correlated with productsListed and 1 other fieldsHigh correlation
seniority is highly overall correlated with seniorityAsMonths and 1 other fieldsHigh correlation
seniorityAsMonths is highly overall correlated with seniority and 1 other fieldsHigh correlation
seniorityAsYears is highly overall correlated with seniority and 1 other fieldsHigh correlation
gender is highly overall correlated with civilityGenderId and 1 other fieldsHigh correlation
civilityGenderId is highly overall correlated with gender and 1 other fieldsHigh correlation
civilityTitle is highly overall correlated with gender and 1 other fieldsHigh correlation
hasAnyApp is highly overall correlated with hasIosAppHigh correlation
hasIosApp is highly overall correlated with hasAnyAppHigh correlation
country is highly imbalanced (51.5%)Imbalance
hasAndroidApp is highly imbalanced (71.9%)Imbalance
hasProfilePicture is highly imbalanced (86.3%)Imbalance
countryCode is highly imbalanced (51.5%)Imbalance
socialNbFollowers is highly skewed (γ1 = 88.81691016)Skewed
socialNbFollows is highly skewed (γ1 = 220.8766787)Skewed
socialProductsLiked is highly skewed (γ1 = 244.1577429)Skewed
productsListed is highly skewed (γ1 = 64.89321853)Skewed
productsSold is highly skewed (γ1 = 41.59563253)Skewed
productsWished is highly skewed (γ1 = 49.25695941)Skewed
productsBought is highly skewed (γ1 = 84.79735987)Skewed
identifierHash has unique valuesUnique
socialProductsLiked has 82987 (83.9%) zerosZeros
productsListed has 97189 (98.3%) zerosZeros
productsSold has 96877 (97.9%) zerosZeros
productsPassRate has 97979 (99.1%) zerosZeros
productsWished has 89612 (90.6%) zerosZeros
productsBought has 93494 (94.5%) zerosZeros

Reproduction

Analysis started2023-01-26 08:09:01.407363
Analysis finished2023-01-26 08:09:42.257607
Duration40.85 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

identifierHash
Real number (ℝ)

Distinct98913
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.692039 × 1015
Minimum-9.2231011 × 1018
Maximum9.2233307 × 1018
Zeros0
Zeros (%)0.0%
Negative49466
Negative (%)50.0%
Memory size772.9 KiB
2023-01-26T13:39:42.399245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-9.2231011 × 1018
5-th percentile-8.3003288 × 1018
Q1-4.6228946 × 1018
median-1.3379888 × 1015
Q34.6163881 × 1018
95-th percentile8.3059843 × 1018
Maximum9.2233307 × 1018
Range-3.1221944 × 1014
Interquartile range (IQR)9.2392827 × 1018

Descriptive statistics

Standard deviation5.3308069 × 1018
Coefficient of variation (CV)-796.58933
Kurtosis-1.2018672
Mean-6.692039 × 1015
Median Absolute Deviation (MAD)4.6192998 × 1018
Skewness0.0011335638
Sum2.1531336 × 1018
Variance2.8417502 × 1037
MonotonicityNot monotonic
2023-01-26T13:39:42.583454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.097895248 × 10181
 
< 0.1%
-8.773752927 × 10181
 
< 0.1%
-4.676244826 × 10181
 
< 0.1%
5.290392635 × 10181
 
< 0.1%
6.984266913 × 10181
 
< 0.1%
-7.469642864 × 10181
 
< 0.1%
3.30562579 × 10181
 
< 0.1%
-1.503210116 × 10181
 
< 0.1%
5.445973843 × 10181
 
< 0.1%
2.428770951 × 10181
 
< 0.1%
Other values (98903) 98903
> 99.9%
ValueCountFrequency (%)
-9.223101126 × 10181
< 0.1%
-9.223057731 × 10181
< 0.1%
-9.222867488 × 10181
< 0.1%
-9.222666406 × 10181
< 0.1%
-9.222346324 × 10181
< 0.1%
-9.222209701 × 10181
< 0.1%
-9.222051377 × 10181
< 0.1%
-9.221953725 × 10181
< 0.1%
-9.221801386 × 10181
< 0.1%
-9.221290544 × 10181
< 0.1%
ValueCountFrequency (%)
9.223330728 × 10181
< 0.1%
9.223304665 × 10181
< 0.1%
9.222858252 × 10181
< 0.1%
9.222779374 × 10181
< 0.1%
9.222469665 × 10181
< 0.1%
9.222181005 × 10181
< 0.1%
9.221787149 × 10181
< 0.1%
9.221187815 × 10181
< 0.1%
9.221113475 × 10181
< 0.1%
9.220908268 × 10181
< 0.1%

type
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size772.9 KiB
user
98913 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters395652
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuser
2nd rowuser
3rd rowuser
4th rowuser
5th rowuser

Common Values

ValueCountFrequency (%)
user 98913
100.0%

Length

2023-01-26T13:39:42.767772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:39:42.867743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
user 98913
100.0%

Most occurring characters

ValueCountFrequency (%)
u 98913
25.0%
s 98913
25.0%
e 98913
25.0%
r 98913
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 395652
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 98913
25.0%
s 98913
25.0%
e 98913
25.0%
r 98913
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 395652
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 98913
25.0%
s 98913
25.0%
e 98913
25.0%
r 98913
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 395652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 98913
25.0%
s 98913
25.0%
e 98913
25.0%
r 98913
25.0%

country
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct200
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size772.9 KiB
France
25135 
Etats-Unis
20602 
Royaume-Uni
11310 
Italie
8015 
Allemagne
6567 
Other values (195)
27284 

Length

Max length38
Median length31
Mean length8.0202804
Min length4

Characters and Unicode

Total characters793310
Distinct characters62
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)< 0.1%

Sample

1st rowRoyaume-Uni
2nd rowMonaco
3rd rowFrance
4th rowEtats-Unis
5th rowEtats-Unis

Common Values

ValueCountFrequency (%)
France 25135
25.4%
Etats-Unis 20602
20.8%
Royaume-Uni 11310
11.4%
Italie 8015
 
8.1%
Allemagne 6567
 
6.6%
Espagne 5706
 
5.8%
Australie 2719
 
2.7%
Danemark 1892
 
1.9%
Suède 1826
 
1.8%
Belgique 1666
 
1.7%
Other values (190) 13475
13.6%

Length

2023-01-26T13:39:43.019042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
france 25135
24.9%
etats-unis 20602
20.4%
royaume-uni 11310
11.2%
italie 8015
 
7.9%
allemagne 6567
 
6.5%
espagne 5706
 
5.6%
australie 2719
 
2.7%
danemark 1892
 
1.9%
suède 1826
 
1.8%
belgique 1666
 
1.6%
Other values (230) 15624
15.5%

Most occurring characters

ValueCountFrequency (%)
a 97270
 
12.3%
e 81974
 
10.3%
n 79820
 
10.1%
s 56683
 
7.1%
t 53924
 
6.8%
i 51865
 
6.5%
r 34060
 
4.3%
- 33448
 
4.2%
U 32032
 
4.0%
l 28628
 
3.6%
Other values (52) 243606
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 623723
78.6%
Uppercase Letter 133944
 
16.9%
Dash Punctuation 33448
 
4.2%
Space Separator 2152
 
0.3%
Other Punctuation 37
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 97270
15.6%
e 81974
13.1%
n 79820
12.8%
s 56683
9.1%
t 53924
8.6%
i 51865
8.3%
r 34060
 
5.5%
l 28628
 
4.6%
c 26344
 
4.2%
u 22729
 
3.6%
Other values (20) 90426
14.5%
Uppercase Letter
ValueCountFrequency (%)
U 32032
23.9%
E 26369
19.7%
F 25813
19.3%
R 12275
 
9.2%
A 10361
 
7.7%
I 8885
 
6.6%
S 3488
 
2.6%
B 3481
 
2.6%
C 2540
 
1.9%
P 2119
 
1.6%
Other values (15) 6581
 
4.9%
Other Punctuation
ValueCountFrequency (%)
' 30
81.1%
. 6
 
16.2%
/ 1
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 33448
100.0%
Space Separator
ValueCountFrequency (%)
2152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 757667
95.5%
Common 35643
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 97270
12.8%
e 81974
 
10.8%
n 79820
 
10.5%
s 56683
 
7.5%
t 53924
 
7.1%
i 51865
 
6.8%
r 34060
 
4.5%
U 32032
 
4.2%
l 28628
 
3.8%
E 26369
 
3.5%
Other values (45) 215042
28.4%
Common
ValueCountFrequency (%)
- 33448
93.8%
2152
 
6.0%
' 30
 
0.1%
. 6
 
< 0.1%
( 3
 
< 0.1%
) 3
 
< 0.1%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 789994
99.6%
None 3316
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 97270
12.3%
e 81974
 
10.4%
n 79820
 
10.1%
s 56683
 
7.2%
t 53924
 
6.8%
i 51865
 
6.6%
r 34060
 
4.3%
- 33448
 
4.2%
U 32032
 
4.1%
l 28628
 
3.6%
Other values (46) 240290
30.4%
None
ValueCountFrequency (%)
è 2289
69.0%
é 803
 
24.2%
É 147
 
4.4%
ï 50
 
1.5%
ÃŽ 19
 
0.6%
ç 8
 
0.2%

language
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size772.9 KiB
en
51564 
fr
26372 
it
7766 
de
7178 
es
6033 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters197826
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowfr
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en 51564
52.1%
fr 26372
26.7%
it 7766
 
7.9%
de 7178
 
7.3%
es 6033
 
6.1%

Length

2023-01-26T13:39:43.190882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:39:43.338347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
en 51564
52.1%
fr 26372
26.7%
it 7766
 
7.9%
de 7178
 
7.3%
es 6033
 
6.1%

Most occurring characters

ValueCountFrequency (%)
e 64775
32.7%
n 51564
26.1%
f 26372
13.3%
r 26372
13.3%
i 7766
 
3.9%
t 7766
 
3.9%
d 7178
 
3.6%
s 6033
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197826
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 64775
32.7%
n 51564
26.1%
f 26372
13.3%
r 26372
13.3%
i 7766
 
3.9%
t 7766
 
3.9%
d 7178
 
3.6%
s 6033
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 197826
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 64775
32.7%
n 51564
26.1%
f 26372
13.3%
r 26372
13.3%
i 7766
 
3.9%
t 7766
 
3.9%
d 7178
 
3.6%
s 6033
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 64775
32.7%
n 51564
26.1%
f 26372
13.3%
r 26372
13.3%
i 7766
 
3.9%
t 7766
 
3.9%
d 7178
 
3.6%
s 6033
 
3.0%

socialNbFollowers
Real number (ℝ)

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4322688
Minimum3
Maximum744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:43.469417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median3
Q33
95-th percentile5
Maximum744
Range741
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.882383
Coefficient of variation (CV)1.1311419
Kurtosis14415.307
Mean3.4322688
Median Absolute Deviation (MAD)0
Skewness88.81691
Sum339496
Variance15.072898
MonotonicityNot monotonic
2023-01-26T13:39:43.776138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 84939
85.9%
4 8219
 
8.3%
5 2720
 
2.7%
6 813
 
0.8%
7 539
 
0.5%
8 336
 
0.3%
9 235
 
0.2%
10 164
 
0.2%
11 121
 
0.1%
12 99
 
0.1%
Other values (80) 728
 
0.7%
ValueCountFrequency (%)
3 84939
85.9%
4 8219
 
8.3%
5 2720
 
2.7%
6 813
 
0.8%
7 539
 
0.5%
8 336
 
0.3%
9 235
 
0.2%
10 164
 
0.2%
11 121
 
0.1%
12 99
 
0.1%
ValueCountFrequency (%)
744 1
< 0.1%
353 1
< 0.1%
205 1
< 0.1%
176 1
< 0.1%
172 1
< 0.1%
167 2
< 0.1%
147 1
< 0.1%
137 1
< 0.1%
131 1
< 0.1%
130 1
< 0.1%

socialNbFollows
Real number (ℝ)

Distinct85
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4256771
Minimum0
Maximum13764
Zeros39
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:43.970520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum13764
Range13764
Interquartile range (IQR)0

Descriptive statistics

Standard deviation52.839572
Coefficient of variation (CV)6.2712553
Kurtosis52718.389
Mean8.4256771
Median Absolute Deviation (MAD)0
Skewness220.87668
Sum833409
Variance2792.0204
MonotonicityNot monotonic
2023-01-26T13:39:44.124165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 94893
95.9%
9 2386
 
2.4%
10 618
 
0.6%
11 260
 
0.3%
12 148
 
0.1%
13 94
 
0.1%
15 55
 
0.1%
14 53
 
0.1%
7 52
 
0.1%
0 39
 
< 0.1%
Other values (75) 315
 
0.3%
ValueCountFrequency (%)
0 39
 
< 0.1%
1 5
 
< 0.1%
2 8
 
< 0.1%
3 6
 
< 0.1%
4 11
 
< 0.1%
5 11
 
< 0.1%
6 7
 
< 0.1%
7 52
 
0.1%
8 94893
95.9%
9 2386
 
2.4%
ValueCountFrequency (%)
13764 1
< 0.1%
8268 1
< 0.1%
3649 1
< 0.1%
2013 1
< 0.1%
500 1
< 0.1%
482 1
< 0.1%
450 1
< 0.1%
431 1
< 0.1%
421 1
< 0.1%
209 1
< 0.1%

socialProductsLiked
Real number (ℝ)

SKEWED  ZEROS 

Distinct420
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4207435
Minimum0
Maximum51671
Zeros82987
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:44.261581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum51671
Range51671
Interquartile range (IQR)0

Descriptive statistics

Standard deviation181.03057
Coefficient of variation (CV)40.950254
Kurtosis67765.241
Mean4.4207435
Median Absolute Deviation (MAD)0
Skewness244.15774
Sum437269
Variance32772.067
MonotonicityNot monotonic
2023-01-26T13:39:44.416463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82987
83.9%
1 5261
 
5.3%
2 1898
 
1.9%
3 1215
 
1.2%
4 973
 
1.0%
5 644
 
0.7%
6 532
 
0.5%
7 436
 
0.4%
8 359
 
0.4%
9 316
 
0.3%
Other values (410) 4292
 
4.3%
ValueCountFrequency (%)
0 82987
83.9%
1 5261
 
5.3%
2 1898
 
1.9%
3 1215
 
1.2%
4 973
 
1.0%
5 644
 
0.7%
6 532
 
0.5%
7 436
 
0.4%
8 359
 
0.4%
9 316
 
0.3%
ValueCountFrequency (%)
51671 1
< 0.1%
16040 1
< 0.1%
7044 1
< 0.1%
5979 1
< 0.1%
5598 1
< 0.1%
5595 1
< 0.1%
5109 1
< 0.1%
3037 1
< 0.1%
2942 1
< 0.1%
2823 1
< 0.1%

productsListed
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.093304217
Minimum0
Maximum244
Zeros97189
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:44.547781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum244
Range244
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0501435
Coefficient of variation (CV)21.972678
Kurtosis5760.3013
Mean0.093304217
Median Absolute Deviation (MAD)0
Skewness64.893219
Sum9229
Variance4.2030886
MonotonicityNot monotonic
2023-01-26T13:39:44.711536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97189
98.3%
1 808
 
0.8%
2 278
 
0.3%
3 150
 
0.2%
4 98
 
0.1%
5 62
 
0.1%
6 45
 
< 0.1%
7 40
 
< 0.1%
8 29
 
< 0.1%
10 22
 
< 0.1%
Other values (55) 192
 
0.2%
ValueCountFrequency (%)
0 97189
98.3%
1 808
 
0.8%
2 278
 
0.3%
3 150
 
0.2%
4 98
 
0.1%
5 62
 
0.1%
6 45
 
< 0.1%
7 40
 
< 0.1%
8 29
 
< 0.1%
9 20
 
< 0.1%
ValueCountFrequency (%)
244 1
< 0.1%
217 1
< 0.1%
202 1
< 0.1%
185 1
< 0.1%
123 1
< 0.1%
122 1
< 0.1%
117 2
< 0.1%
113 1
< 0.1%
102 1
< 0.1%
96 1
< 0.1%

productsSold
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1215917
Minimum0
Maximum174
Zeros96877
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:44.832184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum174
Range174
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1268954
Coefficient of variation (CV)17.492109
Kurtosis2355.6734
Mean0.1215917
Median Absolute Deviation (MAD)0
Skewness41.595633
Sum12027
Variance4.5236838
MonotonicityDecreasing
2023-01-26T13:39:44.948826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 96877
97.9%
1 917
 
0.9%
2 325
 
0.3%
3 154
 
0.2%
4 124
 
0.1%
5 58
 
0.1%
6 58
 
0.1%
7 45
 
< 0.1%
9 42
 
< 0.1%
8 31
 
< 0.1%
Other values (65) 282
 
0.3%
ValueCountFrequency (%)
0 96877
97.9%
1 917
 
0.9%
2 325
 
0.3%
3 154
 
0.2%
4 124
 
0.1%
5 58
 
0.1%
6 58
 
0.1%
7 45
 
< 0.1%
8 31
 
< 0.1%
9 42
 
< 0.1%
ValueCountFrequency (%)
174 1
< 0.1%
170 1
< 0.1%
163 1
< 0.1%
152 1
< 0.1%
125 1
< 0.1%
123 1
< 0.1%
108 1
< 0.1%
106 1
< 0.1%
104 1
< 0.1%
92 1
< 0.1%

productsPassRate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81230273
Minimum0
Maximum100
Zeros97979
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:45.096624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.5002052
Coefficient of variation (CV)10.464332
Kurtosis114.03912
Mean0.81230273
Median Absolute Deviation (MAD)0
Skewness10.667299
Sum80347.3
Variance72.253488
MonotonicityNot monotonic
2023-01-26T13:39:45.249593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97979
99.1%
100 441
 
0.4%
66 63
 
0.1%
50 57
 
0.1%
75 42
 
< 0.1%
83 25
 
< 0.1%
90 25
 
< 0.1%
80 22
 
< 0.1%
85 20
 
< 0.1%
60 16
 
< 0.1%
Other values (62) 223
 
0.2%
ValueCountFrequency (%)
0 97979
99.1%
25 5
 
< 0.1%
28 2
 
< 0.1%
31 1
 
< 0.1%
33 8
 
< 0.1%
35 1
 
< 0.1%
37 2
 
< 0.1%
40 2
 
< 0.1%
41.6 1
 
< 0.1%
42 1
 
< 0.1%
ValueCountFrequency (%)
100 441
0.4%
99 1
 
< 0.1%
98.7 1
 
< 0.1%
98 8
 
< 0.1%
96.4 1
 
< 0.1%
96.2 1
 
< 0.1%
96 5
 
< 0.1%
95 5
 
< 0.1%
94 8
 
< 0.1%
93 12
 
< 0.1%

productsWished
Real number (ℝ)

SKEWED  ZEROS 

Distinct279
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5625954
Minimum0
Maximum2635
Zeros89612
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:45.402495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum2635
Range2635
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.192793
Coefficient of variation (CV)16.122403
Kurtosis3369.1631
Mean1.5625954
Median Absolute Deviation (MAD)0
Skewness49.256959
Sum154561
Variance634.67683
MonotonicityNot monotonic
2023-01-26T13:39:45.564406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89612
90.6%
1 3375
 
3.4%
2 1339
 
1.4%
3 797
 
0.8%
4 526
 
0.5%
5 406
 
0.4%
6 299
 
0.3%
7 252
 
0.3%
8 176
 
0.2%
9 158
 
0.2%
Other values (269) 1973
 
2.0%
ValueCountFrequency (%)
0 89612
90.6%
1 3375
 
3.4%
2 1339
 
1.4%
3 797
 
0.8%
4 526
 
0.5%
5 406
 
0.4%
6 299
 
0.3%
7 252
 
0.3%
8 176
 
0.2%
9 158
 
0.2%
ValueCountFrequency (%)
2635 1
< 0.1%
1916 1
< 0.1%
1900 1
< 0.1%
1842 1
< 0.1%
1820 1
< 0.1%
1783 1
< 0.1%
1622 1
< 0.1%
1295 1
< 0.1%
1225 1
< 0.1%
1113 1
< 0.1%

productsBought
Real number (ℝ)

SKEWED  ZEROS 

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17192887
Minimum0
Maximum405
Zeros93494
Zeros (%)94.5%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:45.717172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum405
Range405
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3322657
Coefficient of variation (CV)13.565294
Kurtosis11871.76
Mean0.17192887
Median Absolute Deviation (MAD)0
Skewness84.79736
Sum17006
Variance5.4394631
MonotonicityNot monotonic
2023-01-26T13:39:45.895552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93494
94.5%
1 3297
 
3.3%
2 845
 
0.9%
3 364
 
0.4%
4 214
 
0.2%
5 139
 
0.1%
6 108
 
0.1%
7 65
 
0.1%
8 52
 
0.1%
9 40
 
< 0.1%
Other values (60) 295
 
0.3%
ValueCountFrequency (%)
0 93494
94.5%
1 3297
 
3.3%
2 845
 
0.9%
3 364
 
0.4%
4 214
 
0.2%
5 139
 
0.1%
6 108
 
0.1%
7 65
 
0.1%
8 52
 
0.1%
9 40
 
< 0.1%
ValueCountFrequency (%)
405 1
< 0.1%
279 1
< 0.1%
174 1
< 0.1%
115 1
< 0.1%
105 1
< 0.1%
93 1
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size772.9 KiB
F
76121 
M
22792 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters98913
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 76121
77.0%
M 22792
 
23.0%

Length

2023-01-26T13:39:46.010253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:39:46.096520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
f 76121
77.0%
m 22792
 
23.0%

Most occurring characters

ValueCountFrequency (%)
F 76121
77.0%
M 22792
 
23.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 98913
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 76121
77.0%
M 22792
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98913
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 76121
77.0%
M 22792
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 76121
77.0%
M 22792
 
23.0%

civilityGenderId
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size772.9 KiB
2
75684 
1
22792 
3
 
437

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters98913
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 75684
76.5%
1 22792
 
23.0%
3 437
 
0.4%

Length

2023-01-26T13:39:46.171194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:39:46.260104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2 75684
76.5%
1 22792
 
23.0%
3 437
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2 75684
76.5%
1 22792
 
23.0%
3 437
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 75684
76.5%
1 22792
 
23.0%
3 437
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 98913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 75684
76.5%
1 22792
 
23.0%
3 437
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 75684
76.5%
1 22792
 
23.0%
3 437
 
0.4%

civilityTitle
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size772.9 KiB
mrs
75684 
mr
22792 
miss
 
437

Length

Max length4
Median length3
Mean length2.7739933
Min length2

Characters and Unicode

Total characters274384
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmr
2nd rowmrs
3rd rowmrs
4th rowmrs
5th rowmrs

Common Values

ValueCountFrequency (%)
mrs 75684
76.5%
mr 22792
 
23.0%
miss 437
 
0.4%

Length

2023-01-26T13:39:46.391618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-26T13:39:46.702269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
mrs 75684
76.5%
mr 22792
 
23.0%
miss 437
 
0.4%

Most occurring characters

ValueCountFrequency (%)
m 98913
36.0%
r 98476
35.9%
s 76558
27.9%
i 437
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 274384
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 98913
36.0%
r 98476
35.9%
s 76558
27.9%
i 437
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 274384
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 98913
36.0%
r 98476
35.9%
s 76558
27.9%
i 437
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 98913
36.0%
r 98476
35.9%
s 76558
27.9%
i 437
 
0.2%

hasAnyApp
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.7 KiB
False
72739 
True
26174 
ValueCountFrequency (%)
False 72739
73.5%
True 26174
 
26.5%
2023-01-26T13:39:46.831912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.7 KiB
False
94094 
True
 
4819
ValueCountFrequency (%)
False 94094
95.1%
True 4819
 
4.9%
2023-01-26T13:39:46.948494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

hasIosApp
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.7 KiB
False
77386 
True
21527 
ValueCountFrequency (%)
False 77386
78.2%
True 21527
 
21.8%
2023-01-26T13:39:47.029693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.7 KiB
True
97018 
False
 
1895
ValueCountFrequency (%)
True 97018
98.1%
False 1895
 
1.9%
2023-01-26T13:39:47.112145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

daysSinceLastLogin
Real number (ℝ)

Distinct699
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean581.29124
Minimum11
Maximum709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:47.232886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile43
Q1572
median694
Q3702
95-th percentile708
Maximum709
Range698
Interquartile range (IQR)130

Descriptive statistics

Standard deviation208.85589
Coefficient of variation (CV)0.35929647
Kurtosis1.3887049
Mean581.29124
Median Absolute Deviation (MAD)11
Skewness-1.6754252
Sum57497260
Variance43620.782
MonotonicityNot monotonic
2023-01-26T13:39:47.388894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
702 3838
 
3.9%
703 3792
 
3.8%
695 3677
 
3.7%
696 3565
 
3.6%
701 3516
 
3.6%
700 3397
 
3.4%
693 3384
 
3.4%
694 3368
 
3.4%
705 3328
 
3.4%
704 3284
 
3.3%
Other values (689) 63764
64.5%
ValueCountFrequency (%)
11 811
0.8%
12 409
0.4%
13 344
0.3%
14 311
 
0.3%
15 235
 
0.2%
16 189
 
0.2%
17 208
 
0.2%
18 151
 
0.2%
19 125
 
0.1%
20 143
 
0.1%
ValueCountFrequency (%)
709 2910
2.9%
708 2857
2.9%
707 2797
2.8%
706 2643
2.7%
705 3328
3.4%
704 3284
3.3%
703 3792
3.8%
702 3838
3.9%
701 3516
3.6%
700 3397
3.4%

seniority
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3063.7719
Minimum2852
Maximum3205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:47.498459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2852
5-th percentile2853
Q12857
median3196
Q33201
95-th percentile3205
Maximum3205
Range353
Interquartile range (IQR)344

Descriptive statistics

Standard deviation168.29862
Coefficient of variation (CV)0.054931838
Kurtosis-1.8165044
Mean3063.7719
Median Absolute Deviation (MAD)8
Skewness-0.42708968
Sum3.0304687 × 108
Variance28324.426
MonotonicityNot monotonic
2023-01-26T13:39:47.597431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3199 6366
 
6.4%
3198 6126
 
6.2%
2857 5984
 
6.0%
2856 5945
 
6.0%
3197 5686
 
5.7%
3196 5577
 
5.6%
3200 5496
 
5.6%
3201 5487
 
5.5%
3205 5310
 
5.4%
2855 5267
 
5.3%
Other values (9) 41669
42.1%
ValueCountFrequency (%)
2852 2506
2.5%
2853 4824
4.9%
2854 5192
5.2%
2855 5267
5.3%
2856 5945
6.0%
2857 5984
6.0%
2858 4848
4.9%
2859 4552
4.6%
3195 5134
5.2%
3196 5577
5.6%
ValueCountFrequency (%)
3205 5310
5.4%
3204 5070
5.1%
3203 4921
5.0%
3202 4622
4.7%
3201 5487
5.5%
3200 5496
5.6%
3199 6366
6.4%
3198 6126
6.2%
3197 5686
5.7%
3196 5577
5.6%

seniorityAsMonths
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.12558
Minimum95.07
Maximum106.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:47.703483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum95.07
5-th percentile95.1
Q195.23
median106.53
Q3106.7
95-th percentile106.83
Maximum106.83
Range11.76
Interquartile range (IQR)11.47

Descriptive statistics

Standard deviation5.6097349
Coefficient of variation (CV)0.054929771
Kurtosis-1.8165054
Mean102.12558
Median Absolute Deviation (MAD)0.27
Skewness-0.42709158
Sum10101548
Variance31.469126
MonotonicityNot monotonic
2023-01-26T13:39:47.805289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
106.63 6366
 
6.4%
106.6 6126
 
6.2%
95.23 5984
 
6.0%
95.2 5945
 
6.0%
106.57 5686
 
5.7%
106.53 5577
 
5.6%
106.67 5496
 
5.6%
106.7 5487
 
5.5%
106.83 5310
 
5.4%
95.17 5267
 
5.3%
Other values (9) 41669
42.1%
ValueCountFrequency (%)
95.07 2506
2.5%
95.1 4824
4.9%
95.13 5192
5.2%
95.17 5267
5.3%
95.2 5945
6.0%
95.23 5984
6.0%
95.27 4848
4.9%
95.3 4552
4.6%
106.5 5134
5.2%
106.53 5577
5.6%
ValueCountFrequency (%)
106.83 5310
5.4%
106.8 5070
5.1%
106.77 4921
5.0%
106.73 4622
4.7%
106.7 5487
5.5%
106.67 5496
5.6%
106.63 6366
6.4%
106.6 6126
6.2%
106.57 5686
5.7%
106.53 5577
5.6%

seniorityAsYears
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5104244
Minimum7.92
Maximum8.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size772.9 KiB
2023-01-26T13:39:47.909847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum7.92
5-th percentile7.92
Q17.94
median8.88
Q38.89
95-th percentile8.9
Maximum8.9
Range0.98
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.46786295
Coefficient of variation (CV)0.054975278
Kurtosis-1.8163167
Mean8.5104244
Median Absolute Deviation (MAD)0.02
Skewness-0.42731135
Sum841791.61
Variance0.21889574
MonotonicityNot monotonic
2023-01-26T13:39:48.010970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8.88 22523
22.8%
8.89 21971
22.2%
7.93 16404
16.6%
7.94 15384
15.6%
8.9 15301
15.5%
7.92 7330
 
7.4%
ValueCountFrequency (%)
7.92 7330
 
7.4%
7.93 16404
16.6%
7.94 15384
15.6%
8.88 22523
22.8%
8.89 21971
22.2%
8.9 15301
15.5%
ValueCountFrequency (%)
8.9 15301
15.5%
8.89 21971
22.2%
8.88 22523
22.8%
7.94 15384
15.6%
7.93 16404
16.6%
7.92 7330
 
7.4%

countryCode
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct199
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size772.9 KiB
fr
25135 
us
20602 
gb
11310 
it
8015 
de
6567 
Other values (194)
27284 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters197826
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowgb
2nd rowmc
3rd rowfr
4th rowus
5th rowus

Common Values

ValueCountFrequency (%)
fr 25135
25.4%
us 20602
20.8%
gb 11310
11.4%
it 8015
 
8.1%
de 6567
 
6.6%
es 5706
 
5.8%
au 2719
 
2.7%
dk 1892
 
1.9%
se 1826
 
1.8%
be 1666
 
1.7%
Other values (189) 13475
13.6%

Length

2023-01-26T13:39:48.133591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fr 25135
25.4%
us 20602
20.8%
gb 11310
11.4%
it 8015
 
8.1%
de 6567
 
6.6%
es 5706
 
5.8%
au 2719
 
2.7%
dk 1892
 
1.9%
se 1826
 
1.8%
be 1666
 
1.7%
Other values (189) 13475
13.6%

Most occurring characters

ValueCountFrequency (%)
s 28735
14.5%
r 26825
13.6%
f 25826
13.1%
u 24113
12.2%
e 16593
8.4%
b 13298
6.7%
g 12103
6.1%
i 9586
 
4.8%
t 9352
 
4.7%
d 8670
 
4.4%
Other values (16) 22725
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197826
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 28735
14.5%
r 26825
13.6%
f 25826
13.1%
u 24113
12.2%
e 16593
8.4%
b 13298
6.7%
g 12103
6.1%
i 9586
 
4.8%
t 9352
 
4.7%
d 8670
 
4.4%
Other values (16) 22725
11.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 197826
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 28735
14.5%
r 26825
13.6%
f 25826
13.1%
u 24113
12.2%
e 16593
8.4%
b 13298
6.7%
g 12103
6.1%
i 9586
 
4.8%
t 9352
 
4.7%
d 8670
 
4.4%
Other values (16) 22725
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 28735
14.5%
r 26825
13.6%
f 25826
13.1%
u 24113
12.2%
e 16593
8.4%
b 13298
6.7%
g 12103
6.1%
i 9586
 
4.8%
t 9352
 
4.7%
d 8670
 
4.4%
Other values (16) 22725
11.5%

Interactions

2023-01-26T13:39:38.880085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:08.550416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:11.326590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:13.813102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:16.746403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:19.324756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:21.870312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:24.186907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:26.412743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:28.872611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:31.584704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:34.213685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:36.623597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:39.035754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:08.747929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:11.567250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:14.019105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:16.949399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:19.557803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:22.065007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:24.387836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:26.564433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:29.113285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:31.781751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:34.392972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:36.804671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:39.229953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:08.936497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:11.751633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:14.247589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:17.147983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:19.775562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:22.202808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:24.587083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:26.736508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:29.356441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:32.002506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:34.565213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:36.981006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:39.418988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:09.129899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:11.915229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:14.502672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:17.310687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:19.979597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:22.375518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:24.746425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:27.073550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:29.555511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:32.226805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:34.764092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:37.134366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:39.647205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:09.300683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:12.103122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:14.697984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:17.514539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:20.146971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:22.511393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:24.878765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:27.235387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:29.769366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:32.411015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:34.996821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:37.293831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:39.810741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:09.642353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:12.287818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:14.935505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:17.713805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:20.343790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:22.737951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:25.026435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:27.408364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:29.957510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:32.646089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:35.213833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:37.424025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:39.955234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:09.873989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:12.493011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:15.129054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:17.912208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:20.477123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:22.935836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:25.178610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:27.592911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:30.149730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:32.988522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:35.395218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:37.578908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:40.088545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:10.067030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:12.714741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:15.345542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:18.099376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:20.602917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:23.117305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:25.348276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:27.768126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:30.327045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:33.135483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:35.580847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:37.729248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:40.251486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:10.244590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:12.874332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:15.749223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:18.277306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:20.742009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:23.315368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:25.494375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:27.924876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:30.535214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:33.344228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:35.745879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:37.908806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:40.401219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:10.463587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:13.020400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:15.965750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:18.516544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:20.965687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:23.546513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:25.709108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:28.109539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:30.718951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:33.523161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:35.921466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:38.059615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:40.572524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:10.681530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:13.219584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:16.162099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:18.724595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:21.145878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:23.730990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:25.905059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:28.291568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:30.905811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:33.703084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:36.120468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:38.383173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:40.713817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:10.898232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:13.411695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:16.352388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:18.933189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:21.375650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:23.880725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:26.092172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:28.484945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:31.106660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:33.931375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:36.270489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:38.534809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:40.874381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:11.078651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:13.613563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:16.545520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:19.104418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:21.664365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:24.040009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:26.246189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:28.676373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:31.362545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:34.070603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:36.454702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-26T13:39:38.702176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-01-26T13:39:48.278647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
identifierHashsocialNbFollowerssocialNbFollowssocialProductsLikedproductsListedproductsSoldproductsPassRateproductsWishedproductsBoughtdaysSinceLastLoginseniorityseniorityAsMonthsseniorityAsYearslanguagegendercivilityGenderIdcivilityTitlehasAnyApphasAndroidApphasIosApphasProfilePicture
identifierHash1.000-0.001-0.0020.003-0.0010.0030.0010.000-0.001-0.0010.0020.0020.0020.0000.0030.0060.0060.0000.0000.0040.000
socialNbFollowers-0.0011.0000.2740.2560.2720.3270.2560.2280.302-0.207-0.025-0.025-0.0280.0040.0000.0130.0130.0200.0090.0170.087
socialNbFollows-0.0020.2741.0000.3110.1460.1620.1360.3070.264-0.200-0.000-0.000-0.0000.0020.0020.0000.0000.0080.0130.0080.039
socialProductsLiked0.0030.2560.3111.0000.1780.1860.1540.4760.313-0.4010.0040.0040.0040.0030.0040.0000.0000.0060.0000.0080.046
productsListed-0.0010.2720.1460.1781.0000.5520.5330.1370.122-0.1880.0010.0010.0000.0110.0060.0340.0340.0250.0110.0230.098
productsSold0.0030.3270.1620.1860.5521.0000.6810.1370.144-0.1980.0020.0020.0030.0160.0000.0340.0340.0440.0120.0440.147
productsPassRate0.0010.2560.1360.1540.5330.6811.0000.1160.112-0.1510.0000.0000.0010.0260.0000.0550.0550.0970.0330.0920.279
productsWished0.0000.2280.3070.4760.1370.1370.1161.0000.350-0.3150.0030.0030.0040.0020.0000.0150.0150.0300.0210.0250.091
productsBought-0.0010.3020.2640.3130.1220.1440.1120.3501.000-0.2620.0080.0080.0070.0000.0000.0140.0140.0160.0000.0170.059
daysSinceLastLogin-0.001-0.207-0.200-0.401-0.188-0.198-0.151-0.315-0.2621.0000.3680.3680.3610.0310.0330.0580.0580.2340.0880.2240.224
seniority0.002-0.025-0.0000.0040.0010.0020.0000.0030.0080.3681.0001.0000.9840.0250.0000.0000.0000.0040.0070.0000.000
seniorityAsMonths0.002-0.025-0.0000.0040.0010.0020.0000.0030.0080.3681.0001.0000.9840.0250.0000.0000.0000.0040.0070.0000.000
seniorityAsYears0.002-0.028-0.0000.0040.0000.0030.0010.0040.0070.3610.9840.9841.0000.0250.0000.0000.0000.0040.0070.0000.000
language0.0000.0040.0020.0030.0110.0160.0260.0020.0000.0310.0250.0250.0251.0000.0740.0560.0560.0920.1480.0780.037
gender0.0030.0000.0020.0040.0060.0000.0000.0000.0000.0330.0000.0000.0000.0741.0001.0001.0000.0880.0450.0720.004
civilityGenderId0.0060.0130.0000.0000.0340.0340.0550.0150.0140.0580.0000.0000.0000.0561.0001.0001.0000.0910.0560.0730.074
civilityTitle0.0060.0130.0000.0000.0340.0340.0550.0150.0140.0580.0000.0000.0000.0561.0001.0001.0000.0910.0560.0730.074
hasAnyApp0.0000.0200.0080.0060.0250.0440.0970.0300.0160.2340.0040.0040.0040.0920.0880.0910.0911.0000.3770.8790.139
hasAndroidApp0.0000.0090.0130.0000.0110.0120.0330.0210.0000.0880.0070.0070.0070.1480.0450.0560.0560.3771.0000.1000.038
hasIosApp0.0040.0170.0080.0080.0230.0440.0920.0250.0170.2240.0000.0000.0000.0780.0720.0730.0730.8790.1001.0000.133
hasProfilePicture0.0000.0870.0390.0460.0980.1470.2790.0910.0590.2240.0000.0000.0000.0370.0040.0740.0740.1390.0380.1331.000

Missing values

2023-01-26T13:39:41.175518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-26T13:39:41.788569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

identifierHashtypecountrylanguagesocialNbFollowerssocialNbFollowssocialProductsLikedproductsListedproductsSoldproductsPassRateproductsWishedproductsBoughtgendercivilityGenderIdcivilityTitlehasAnyApphasAndroidApphasIosApphasProfilePicturedaysSinceLastLoginseniorityseniorityAsMonthsseniorityAsYearscountryCode
0-1097895247965112460userRoyaume-Unien14710772617474.01041M1mrTrueFalseTrueTrue113196106.538.88gb
12347567364561867620userMonacoen167821917099.000F2mrsTrueFalseTrueTrue123204106.808.90mc
26870940546848049750userFrancefr13713603316394.0103F2mrsTrueFalseTrueFalse113203106.778.90fr
3-4640272621319568052userEtats-Unisen131101412215292.070F2mrsTrueFalseTrueFalse123198106.608.88us
4-5175830994878542658userEtats-Unisen1678025125100.000F2mrsFalseFalseFalseTrue22285495.137.93us
57631788075812383072userAllemagnede1301214712391.000F2mrsTrueFalseTrueFalse113196106.538.88de
6674361423306028463userSuèdeen121011403110894.0531105F3missTrueTrueFalseFalse113198106.608.88se
72550976450216757005userFrancefr5393510698.000F2mrsTrueFalseTrueTrue11285795.237.94fr
83718185418791028367userItalieit7441376451671010485.018420F2mrsTrueFalseTrueFalse143195106.508.88it
93908244093584862523userRoyaume-Unien578451239274.062F3missTrueFalseTrueTrue11285695.207.93gb
identifierHashtypecountrylanguagesocialNbFollowerssocialNbFollowssocialProductsLikedproductsListedproductsSoldproductsPassRateproductsWishedproductsBoughtgendercivilityGenderIdcivilityTitlehasAnyApphasAndroidApphasIosApphasProfilePicturedaysSinceLastLoginseniorityseniorityAsMonthsseniorityAsYearscountryCode
98903-2219367748414812248userEspagnees380000.000F2mrsTrueFalseTrueTrue1123204106.88.9es
989042896867688384676348userRoyaume-Unien380000.000F2mrsFalseFalseFalseTrue7083204106.88.9gb
989053164321379397826945userEtats-Unisen386000.000F2mrsFalseFalseFalseTrue6553204106.88.9us
98906-3379431417039360607userIrlandeen380000.000F2mrsFalseFalseFalseTrue7083204106.88.9ie
98907-5212100190867739388userEtats-Unisen380000.000F2mrsFalseFalseFalseTrue7083204106.88.9us
98908-5324380437900495747userEtats-Unisfr380000.000M1mrFalseFalseFalseTrue7083204106.88.9us
98909-5607668753771114442userFrancefr380000.000M1mrTrueFalseTrueTrue6953204106.88.9fr
98910350630276238833248userBelgiqueen380000.000M1mrTrueTrueFalseTrue5203204106.88.9be
989112006580738726207028userItalieit380000.000F2mrsFalseFalseFalseTrue2673204106.88.9it
98912-7621316584087253691userGuinéefr380000.000M1mrTrueFalseTrueTrue5613204106.88.9gn